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Sökning: WAKA:kon > Högskolan i Borås > Johansson Ulf

  • Resultat 1-10 av 41
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1.
  • Gabrielsson, Patrick, et al. (författare)
  • Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution
  • 2014
  • Konferensbidrag (refereegranskat)abstract
    • Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility of evolving transparent entry- and exit trading strategies for the E-mini S&P 500 index futures market in a high-frequency trading environment using grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.
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2.
  • Gabrielsson, Patrick, et al. (författare)
  • Evolving Hierarchical Temporal Memory-Based Trading Models
  • 2013
  • Konferensbidrag (refereegranskat)abstract
    • We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.
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3.
  • Gabrielsson, Patrick, et al. (författare)
  • Hierarchical Temporal Memory-based algorithmic trading of financial markets
  • 2012
  • Konferensbidrag (refereegranskat)abstract
    • This paper explores the possibility of using the Hierarchical Temporal Memory (HTM) machine learning technology to create a profitable software agent for trading financial markets. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as features vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was divided into a training set, a validation set and three test sets; bearish, bullish and horizontal. The best performing model on the validation set was tested on the three test sets. Artificial Neural Networks (ANNs) were subjected to the same data sets in order to benchmark HTM performance. The results suggest that the HTM technology can be used together with a feature vector of technical indicators to create a profitable trading algorithm for financial markets. Results also suggest that HTM performance is, at the very least, comparable to commonly applied neural network models.
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4.
  • Giri, Chandadevi, et al. (författare)
  • Data-driven Business Understanding in the Fashion and Apparel Industry
  • 2021
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Data analytics is pervasive in retailing as a key tool to gain customer insights. Often, the data sets used are large, but also rich, i.e., they contain specific information, including demographic details, about individual customers. Typical usage of the analytics include personalized recommendations, churn prediction and estimating customer life-time value. In this application paper, an investigation is carried out using a very large real-world data set from the fashion retailing industry, containing only limited information. Specifically, while the purchases can be connected to individual customers, there is no additional information available about the customers. With this in mind, the main purpose is to discover what the company can learn about their business and their customers as a group, based on the available data. The exploratory analysis uses data from four years, where each year has more than 1 million customers and 6 million transactions. Using traditional RFM (Recency, Frequency and Monetary) analysis, including looking at the transitions between different segments between two years, some interesting patterns can be observed. As an example, more than half of the customers are replaced each year. In a second experiment, the possibility to predict which of the customers that are the most likely to not make a purchase the next year is examined. Interestingly enough, while the two algorithms evaluated obtained very similar f-measures; the random forest had a substantially higher precision, while the gradient boosting showed clearly better recall. In the last experiment, targeting only the customers that have remained loyal for at least three years, rule sets describing patterns and trends that are strong indicators for churn or not are inspected and analyzed.
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5.
  • Giri, Chandadevi, et al. (författare)
  • Predictive modeling of campaigns to quantify performance in fashion retail industry
  • 2019
  • Ingår i: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. - : IEEE. - 9781728108582 - 9781728108599 ; , s. 2267-2273
  • Konferensbidrag (refereegranskat)abstract
    • Managing campaigns and promotions effectively is vital for the fashion retail industry. While retailers invest a lot of money in campaigns, customer retention is often very low. At innovative retailers, data-driven methods, aimed at understanding and ultimately optimizing campaigns are introduced. In this application paper, machine learning techniques are employed to analyze data about campaigns and promotions from a leading Swedish e-retailer. More specifically, predictive modeling is used to forecast the profitability and activation of campaigns using different kinds of promotions. In the empirical investigation, regression models are generated to estimate the profitability, and classification models are used to predict the overall success of the campaigns. In both cases, random forests are compared to individual tree models. As expected, the more complex ensembles are more accurate, but the usage of interpretable tree models makes it possible to analyze the underlying relationships, simply by inspecting the trees. In conclusion, the accuracy of the predictive models must be deemed high enough to make these data-driven methods attractive.
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6.
  • Johansson, Ulf, et al. (författare)
  • Accurate and Interpretable Regression Trees using Oracle Coaching
  • 2014
  • Konferensbidrag (refereegranskat)abstract
    • In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The over- all purpose of oracle coaching is to reduce this accuracy vs. comprehensibility trade-off by producing interpretable models optimized for the specific production set at hand. The method requires production set inputs to be present when generating the predictive model, a demand fulfilled in most, but not all, predic- tive modeling scenarios. In oracle coaching, a highly accurate, but opaque, model is first induced from the training data. This model (“the oracle”) is then used to label both the training instances and the production instances. Finally, interpretable models are trained using different combinations of the resulting data sets. In this paper, the oracle coaching produces regression trees, using neural networks and random forests as oracles. The experiments, using 32 publicly available data sets, show that the oracle coaching leads to significantly improved predictive performance, compared to standard induction. In addition, it is also shown that a highly accurate opaque model can be successfully used as a pre- processing step to reduce the noise typically present in data, even in situations where production inputs are not available. In fact, just augmenting or replacing training data with another copy of the training set, but with the predictions from the opaque model as targets, produced significantly more accurate and/or more compact regression trees.
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7.
  • Johansson, Ulf, et al. (författare)
  • Conformal Prediction for Accuracy Guarantees in Classification with Reject Option
  • 2023
  • Ingår i: Modeling Decisions for Artificial Intelligence. - : Springer. - 9783031334979 ; , s. 133-145, s. 133-145
  • Konferensbidrag (refereegranskat)abstract
    • A standard classifier is forced to predict the label of every test instance, even when confidence in the predictions is very low. In many scenarios, it would, however, be better to avoid making these predictions, maybe leaving them to a human expert. A classifier with that alternative is referred to as a classifier with reject option. In this paper, we propose an algorithm that, for a particular data set, automatically suggests a number of accuracy levels, which it will be able to meet perfectly, using a classifier with reject option. Since the basis of the suggested algorithm is conformal prediction, it comes with strong validity guarantees. The experimentation, using 25 publicly available two-class data sets, confirms that the algorithm obtains empirical accuracies very close to the requested levels. In addition, in an outright comparison with probabilistic predictors, including models calibrated with Platt scaling, the suggested algorithm clearly outperforms the alternatives.
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9.
  • Johansson, Ulf, et al. (författare)
  • Evaluating Ensembles on QSAR Classification
  • 2009
  • Konferensbidrag (refereegranskat)abstract
    • Novel, often quite technical algorithms, for ensembling artificial neural networks are constantly suggested. Naturally, when presenting a novel algorithm, the authors, at least implicitly, claim that their algorithm, in some aspect, represents the state-of-the-art. Obviously, the most important criterion is predictive performance, normally measured using either accuracy or area under the ROC-curve (AUC). This paper presents a study where the predictive performance of two widely acknowledged ensemble techniques; GASEN and NegBagg, is compared to more straightforward alternatives like bagging. The somewhat surprising result of the experimentation using, in total, 32 publicly available data sets from the medical domain, was that both GASEN and NegBagg were clearly outperformed by several of the straightforward techniques. One particularly striking result was that not applying the GASEN technique; i.e., ensembling all available networks instead of using the subset suggested by GASEN, turned out to produce more accurate ensembles.
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  • Resultat 1-10 av 41

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